About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.
The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.
Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests. According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said co-author Peter Fedichev, Science Director at GERO. "Inspired by these examples, we explored AI potential for'Health Risks Assessment' based on human physical activity," Fedichev added. For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES). They trained neural network to predict biological age and mortality risk of the participants from one-week long stream of activity measurements.
IMAGE: This is a screenshot of the Gero Lifespan app. Moscow, March 29, 2018 - Researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT) have shown that physical activity data acquired from wearables can be used to produce digital biomarkers of aging and frailty. Many physiological parameters demonstrate tight correlations with age. Various biomarkers of age, such as DNA methylation, gene expression or circulating blood factor levels could be used to build accurate «biological clocks» to obtain individual biological age and the rate of aging estimations. Yet large-scale biochemical or genomic profiling is still logistically difficult and expensive for any practical applications beyond academic research.
In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect answers for both 2D and 3D questions. Using the classification technique, the impacts of 2D and 3D multimedia educational contents on learning, memory retention and recall will be compared. The subjects learn similar 2D and 3D educational contents. Afterwards, subjects are asked 20 multiple-choice questions (MCQs) associated with the contents after thirty minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers. Excellent accuracies obtained by both classifiers and by applying statistical analysis on the results, no significant difference is indicated between 2D and 3D multimedia educational contents on learning, memory retention and recall in both STM and LTM.